Assessing Consistency in Single-Case A-B-A-B Phase Designs.
Add CONDAP and CONEFF calculations to your next A-B-A-B study to quantify consistency beyond visual inspection.
01Research in Context
What this study did
Tanious et al. (2020) built two new math tools for A-B-A-B graphs. The tools are called CONDAP and CONEFF. They tell you how steady each phase is and how cleanly the data jump when you switch phases.
The paper shows the formulas and walks through two pretend data sets. No real kids or clients were tested. The goal is to give you numbers that back up, or challenge, what your eyes see.
What they found
The authors show that visual picks ("looks like a change") can disagree with the new indices. In one fake set the line seems better, but CONEFF says the change is weak. The tools give a single value you can report or graph next to the raw data.
How this fits with other research
Manolov et al. (2022) also push for planning before you graph. Their flowchart helps you pick an effect measure before data collection. René et al. finish the job by giving you indices to run after the data are in.
Ninci (2023) warns that our eyes miss trends and drift. The new CONDAP/CONEFF numbers can act as a safety check against those very visual mistakes.
Young (2019) offers a free Monte Carlo app that gives p-values for single-case data. You can use both tools: René’s indices for consistency, Young’s app for statistical significance.
Why it matters
Next time you run an A-B-A-B on nail-biting, walking, or math facts, plug the phase means and trends into the CONDAP/CONEFF formulas. If the numbers are low, slow down before you claim victory. Add these two lines to your report and reviewers, parents, or teachers see right away how steady—or shaky—your effect is.
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02At a glance
03Original abstract
Previous research has introduced several effect size measures (ESMs) to quantify data aspects of single-case experimental designs (SCEDs): level, trend, variability, overlap, and immediacy. In the current article, we extend the existing literature by introducing two methods for quantifying consistency in single-case A-B-A-B phase designs. The first method assesses the consistency of data patterns across phases implementing the same condition, called CONsistency of DAta Patterns (CONDAP). The second measure assesses the consistency of the five other data aspects when changing from baseline to experimental phase, called CONsistency of the EFFects (CONEFF). We illustrate the calculation of both measures for four A-B-A-B phase designs from published literature and demonstrate how CONDAP and CONEFF can supplement visual analysis of SCED data. Finally, we discuss directions for future research.
Behavior modification, 2020 · doi:10.1177/0145445519837726